[1]} cast document ranking as a pair prediction task that predicts a document to be relevant or not for an input query, and fine-tune a pre-trained BERT model using the pair prediction formulation, as illustrated in Figure REF  (Middle). Specifically, we feed the concatenation of query \(Q\) , a [SEP] token, and document \(D\)  to the ranker, and the relevance label \(y \in \lbrace 1, 0\rbrace \)  is predicted using a logistic regression layer with softmax as
\(P_r(y|Q, D) = \text{softmax}(\mathbf {W} \cdot \mathbf {x}),\) 
